code: def test_fit(self):
      tf_enc = TensorflowMoleculeEncoder.zinc_encoder()
  
      smiles = [
          "Cn1cnc2c1c(=O)n(C)c(=O)n2C",
          "O=C(O)[C@@H]1/C(=C/CO)O[C@@H]2CC(=O)N21",
          "Cn1c2nncnc2c(=O)n(C)c1=O",
          "Cn1cnc2c1c(=O)[nH]c(=O)n2C",
          "NC(=O)c1ncc[nH]c1=O",
          "O=C1OCc2c1[nH]c(=O)[nH]c2=O",
          "Cn1c(N)c(N)c(=O)n(C)c1=O",
          "CNc1nc2c([nH]1)c(=O)[nH]c(=O)n2C",
          "CC(=O)N1CN(C(C)=O)[C@@H](O)[C@@H]1O",
          "CC(=O)N1CN(C(C)=O)[C@H](O)[C@H]1O",
          "Cc1[nH]c(=O)[nH]c(=O)c1CO",
          "O=C1NCCCc2c1no[n+]2[O-]",
          "Cc1nc(C(N)=O)c(N)n1CCO",
          "O=c1[nH]cc(N2CCOCC2)c(=O)[nH]1",
      ]
  
      featurizer = dc.feat.one_hot.OneHotFeaturizer(zinc_charset, 120)
      mols = [Chem.MolFromSmiles(x) for x in smiles]
      features = featurizer.featurize(mols)
  
      dataset = DiskDataset.from_numpy(features, features)
      prediction = tf_enc.predict_on_batch(dataset.X)
      tf_de = TensorflowMoleculeDecoder.zinc_decoder()
      one_hot_decoded = tf_de.predict_on_batch(prediction)
      decoded_smiles = featurizer.untransform(one_hot_decoded)
      assert len(decoded_smiles) == len(smiles)
  
file_path: deepchem/contrib/autoencoder_models/test_tensorflowEncoders.py
imports: from unittest import TestCase
  import pytest
  from rdkit import Chem
  import deepchem
  from deepchem.data import DiskDataset
  from deepchem.feat.one_hot import zinc_charset
  from deepchem.models.autoencoder_models.autoencoder import TensorflowMoleculeEncoder, TensorflowMoleculeDecoder
label_desc: Use image node_id 1 for calling the TestTensorflowEncoders obj's underlying member method code with example usage: obj.test_fit() without return types